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Estimating Risk of Dynamic Trading Strategies from High Frequency Data Flow

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Advances in Data Mining: Applications and Theoretical Aspects (ICDM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9165))

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Abstract

We consider the problem of risk management in the framework of low latency trading. We suggest an efficient method of real-time analysis of massive data flow from the market. The result of the analysis is a new risk measure Dynamic VaR (DVaR) for risk management of low latency trading robots. The work of DVaR is illustrated on a test example and compared with Traditional VaR and ex-post measure commonly used in high frequency trading.

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Authors and Affiliations

  1. University of Chicago, Chicago, 60637, USA

    Yuri Balasanov

  2. Lomonosov Moscow State University, Moscow, 119991, Russia

    Alexander Doynikov, Victor Lavrent’ev & Leonid Nazarov

Authors
  1. Yuri Balasanov
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  2. Alexander Doynikov
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  3. Victor Lavrent’ev
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  4. Leonid Nazarov
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Corresponding author

Correspondence to Yuri Balasanov .

Editor information

Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

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Balasanov, Y., Doynikov, A., Lavrent’ev, V., Nazarov, L. (2015). Estimating Risk of Dynamic Trading Strategies from High Frequency Data Flow. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-20910-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20909-8

  • Online ISBN: 978-3-319-20910-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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